About ConnectomeDB

Before using this resource, please read our Terms & Conditions.

ConnectomeDB is a comprehensive and ongoing project that provides a high-quality manually curated database of interacting ligand-receptor pairs for use in cell-cell communication analysis. First released in 2015 (Ramilowski, et al.), and subsequently updated in 2020 (Hou, et al.) and 2025 (Liu, et al.), it aims to enhance the understanding of cell-cell communication in humans and other mammals, supporting biological and medical research.

Developers: YCU Bioinformatics Lab and Perkins Systems Biology & Genomics Lab.

ConnectomeDB2025 - Database & Online Resource for Cell-to-Cell Communication Predictions

  • **3,448 Manually Curated Ligand–Receptor Pairs (Primarily Human; includes 12 Mouse-specific): comprehensive and most accurate with primary literature support
  • 1,093 Ligands & 867 Receptors: metadata and functional annotations
  • Homologous Interactions: ~98% LR pairs in mouse and in 11 other vertebrate species
  • Free, user-friendly resource: developed with Quarto 1.7+ and BioRender

Publication: ConnectomeDB2025, a high quality manually curated ligand-receptor database for cell-to-cell communication prediction, P Liu, et al. (TBA)
GitHub repo: https://github.com/bioinfo-YCU/ConnectomeDB (open upon publication)

Want to learn more about connectomeDB2025? Please refer to the Methods section.

Access ConnectomeDB2025 Online

An internet connection and a web browser are required. No username and no password are required. The following major operating systems and web browsers have been tested and are guaranteed to work:

  • Windows 10/11: Google Chrome, Microsoft Edge
  • macOS 15+: Google Chrome, Safari
  • mobile devices: Google Chrome, Safari

Not familiar with ConnectomeDB 2025 or want to learn more? Please check out our tutorials (currently under construction).

Network Analysis Toolkit for Multicellular Interactions (NATMI)

NATMI is a Python-based toolkit designed for constructing and analyzing intercellular communication networks across multiple biological scales—from cellular niches and tissues to whole-organism systems.

  • Predicts ligand–receptor interactions driving cell-to-cell communication
  • Works with both single-cell and bulk gene expression as well as proteomics data
  • Enables network-level insights into cell communication dynamics across diverse biological contexts
  • Supports multiple species, including humans and other vertebrates

Publication: Predicting cell-to-cell communication networks using NATMI (Nat commun, 2020)
GitHub repo: https://github.com/forrest-lab/NATMI

Please cite the following papers if you use ConnectomeDB or NATMI in your publication:

  1. ConnectomeDB2025, a high quality manually curated ligand-receptor database for cell-to-cell communication prediction, P Liu, et al. (TBA)
    PMID: (TBA); DOI: (TBA)

  2. Predicting cell-to-cell communication networks using NATMI, R Hou, et al. Nature communications 11(1), 5011, 2020
    PMID: 33024107; DOI: 10.1038/s41467-020-18873-z

  3. A draft network of ligand–receptor-mediated multicellular signalling in human, JA Ramilowski, et al. Nature communications 6(1), 7866, 2015
    PMID: 26198319; DOI: 10.1038/ncomms8866

Inquiries

General inquiries: send Alistair, Jordan, and Rui an email.
Online resource issues: fill out this Inquiry form or contact us via this email.

Feedback

We welcome your feedback and suggestions for improving ConnectomeDB content and would be glad to hear any ideas for further development.
If you would like to contribute to the ConnectomeDB project, please visit the GitHub repository (open upon publication) or email us.